Together AI Interview Guide 2026: Open-Model Inference, CUDA Kernels, Speculative Decoding, and Enterprise AI

Together AI Interview Process: Complete 2026 Guide

Overview

Together AI is the cloud platform for open and custom AI — fast inference for open-source models (Llama, Mixtral, DeepSeek, Qwen), fine-tuning infrastructure, and custom deployment for enterprises wanting to control their model stack. Founded 2022 by Vipul Ved Prakash and Ce Zhang (based on research work at Stanford), private with multiple rapid funding rounds through the AI boom (Series A 2023, B 2024, continued through 2025). ~200 employees in 2026, headquartered in San Francisco with engineering distributed across the US and remote hires globally. Together AI differentiates from competitors (Modal, Replicate, Anyscale) through a focus on inference-speed engineering (proprietary optimizations on top of vLLM-style serving), research output (FlashAttention-adjacent papers, serving papers), and enterprise-oriented deployment options including self-hosted and dedicated-endpoint offerings. Interviews reflect the reality of running cutting-edge inference infrastructure — expect serious ML-systems depth, GPU optimization knowledge, and an academic-adjacent engineering culture.

Interview Structure

Recruiter screen (30 min): background, why Together, team interest. The engineering surface is cutting-edge AI infrastructure: inference engine (CUDA kernels, attention kernels, batching), training infrastructure (distributed training, fine-tuning), platform (API, SDK, billing), data / ML research, and enterprise deployment (self-hosted, dedicated endpoints). Triage matters — inference-engine work requires different depth than platform-API work.

Technical phone screen (60 min): one coding problem, medium-hard. Python / C++ / CUDA depending on role. Problems tilt toward ML-systems applied — implement an attention primitive, handle streaming generation, batch requests efficiently.

Take-home (many senior / research roles): 4–8 hours on a realistic engineering problem. For inference-engine roles, often involves implementing or optimizing a CUDA kernel; for platform, a focused systems problem.

Onsite / virtual onsite (4–5 rounds):

• Coding (2 rounds): one algorithms round, one applied ML-systems round. Difficulty is solidly hard — comparable to Nvidia or Anthropic inference teams.
• System design (1 round): inference / training infrastructure prompts. “Design the inference-serving system supporting 100+ open-source models with efficient GPU sharing.” “Design the fine-tuning service with multi-tenant LoRA adapter management.” “Design the speculative-decoding integration across diverse model families.”
• ML / research round (1–2 rounds for research-engineering): paper deep-dive, inference-optimization discussion, experiment design. Research roles face two of these.
• Behavioral / hiring manager: past projects, comfort with fast-paced AI-infrastructure environment, academic-engineering collaboration.

Technical Focus Areas

Coding: Python fluency for most roles, C++ / CUDA for inference-kernel and high-performance serving, Go / Rust for some infrastructure, TypeScript for product / API surfaces. Code quality and performance awareness both matter.

Inference optimization: continuous batching (vLLM PagedAttention style), KV-cache management with FlashAttention, speculative decoding (draft model, target model, verification), quantization (INT8, FP8, FP4), tensor parallelism for inference, model-specific kernel optimizations (Mixtral MoE routing, Llama RMSNorm, RoPE positional encoding).

GPU / CUDA programming: for inference-engine roles, real CUDA fluency is required. Memory hierarchy (registers, shared memory, HBM), warp-level primitives, occupancy optimization, Tensor Core utilization, async memory copies.

Distributed training / fine-tuning: LoRA and QLoRA for efficient fine-tuning, distributed-training frameworks (FSDP, DeepSpeed), checkpoint management, gradient accumulation, data-pipeline engineering for training.

Model diversity: Together AI hosts many open-source models with distinct architectural characteristics. Engineers need to reason about supporting Llama-family, MoE models (Mixtral, DeepSeek-V3), Mamba / SSM models, and emerging architectures with a unified serving stack.

Platform / API: OpenAI-compatible API (for easy customer migration from OpenAI), streaming response handling, model catalog management, multi-tenant rate limiting, dedicated endpoints vs serverless trade-offs.

Enterprise deployment: self-hosted options, dedicated-endpoint management, compliance (SOC 2, HIPAA for relevant customers), data-residency controls, custom model deployment for enterprise customers.

Coding Interview Details

Two coding rounds, 60 minutes each. Difficulty is solidly hard — comparable to Nvidia core GPU teams or mid-frontier-lab inference teams. Research-engineering candidates face CUDA or C++ implementation questions.

Typical problem shapes:

• Implement attention from scratch with appropriate batching considerations. Interviewers watch for scaled dot-product mechanics, causal masking, and numerical stability in the softmax — subtracting the row max before exponentiating is the step people skip under pressure.
• Write a CUDA kernel for a specific operation (element-wise, reduction, softmax). Expect follow-ups on memory coalescing, using shared memory for the reduction, and avoiding warp divergence; softmax is a favorite because it forces a reduction and an element-wise pass in one kernel.
• Design a batching scheduler matching incoming requests to available GPU capacity with fairness. The interesting part is continuous batching — admitting new requests into a running batch as earlier sequences finish — and deciding how to preempt or queue when KV-cache memory runs short.
• Streaming token generation with cancellation handling. Focus on propagating a client disconnect back to the scheduler so you stop spending GPU cycles on a dropped request, and on flushing partial output without corrupting the token stream.
• Classic algorithm problems (graphs, DP, priority queues) with ML-systems applied twists. The twist usually maps a systems problem onto a familiar structure — request scheduling as a priority queue, dependency resolution as a graph traversal — so name the underlying pattern first, then handle the domain constraints.

System Design Interview

One round, 60 minutes. Prompts focus on AI-infrastructure realities:

• “Design the inference-serving system supporting 100+ open-source models with shared GPU capacity.”
• “Design the fine-tuning service with multi-tenant LoRA adapter management and per-customer isolation.”
• “Design the speculative-decoding pipeline working across diverse model architectures.”
• “Design the custom-deployment offering for enterprise customers with dedicated capacity.”

What works: specific numbers (H100 / Blackwell memory capacity, typical KV-cache footprint per model, latency budgets for different workloads), engagement with publicly-known research (vLLM’s PagedAttention, FlashAttention variants, Together AI’s own published optimizations), and enterprise-reality awareness. What doesn’t: generic “design a serverless platform” without engaging with AI-specific constraints.

ML / Research Round

For research-engineering roles. Sample topics:

• Walk through a Together AI paper or publication you know well (the company has published serving and training papers).
• Discuss the trade-offs between speculative-decoding approaches. Compare a separate small draft model against self-speculation methods like Medusa that add extra decoding heads, and reason about acceptance rate versus draft overhead — a fast draft that gets rejected often ends up slower than no speculation at all.
• Reason about the trade-offs of different quantization schemes (INT8 vs FP8 vs mixed precision). The probe is usually where accuracy degrades — outlier activations, per-channel versus per-tensor scaling — and which tensors you quantize (weights, activations, or the KV cache) and why.
• Design an experiment to evaluate a new inference-optimization technique. Pick metrics that separate throughput from latency (tokens per second at a fixed batch size, p50 and p99 time-to-first-token), hold the model and hardware fixed, and say how you would confirm output quality did not regress.

Candidates with publication history or production LLM-inference experience have clear edges. Strong ML-systems generalists close the gap with focused prep.

Behavioral Interview

Key themes:

• Shipping velocity: “Tell me about the fastest meaningful improvement you’ve shipped to a production AI system.”
• Academic / engineering balance: “How do you balance research-informed work with shipping production systems?”
• Technical ownership: “Describe a production ML system you owned end-to-end.”
• Customer empathy: “Tell me about a time you engaged with an enterprise customer’s AI-infrastructure problem.”

Preparation Strategy

Weeks 4-8 out: CUDA programming if targeting inference-engine roles. Programming Massively Parallel Processors (Hwu, Kirk, Hajj). LeetCode medium/hard in Python or C++ depending on target.

Weeks 2-4 out: read about LLM inference optimization. vLLM documentation, PagedAttention paper, FlashAttention papers (1, 2, 3), speculative-decoding papers (the original 2022 Meta paper, Medusa, Lookahead). Read Together AI’s publications (their blog publishes depth).

Weeks 1-2 out: use Together AI’s API for real workloads. Compare performance vs OpenAI / Anthropic for specific tasks. Form opinions about the product. Prepare behavioral stories with ML-systems angles.

Day before: review inference-optimization fundamentals; prepare research / product opinions; review behavioral stories.

Difficulty: 8/10

Hard. Inference-engine and research-engineering roles approach frontier-lab rigor. Platform / applied-engineering roles are medium-hard. The specialty depth filter is real — candidates without ML-systems background struggle on core teams. The academic-adjacent culture filter also matters — engineers without research engagement often don’t fit.

Compensation (2025 data, US engineering roles)

• Software Engineer: $185k–$230k base, $200k–$380k equity (4 years), modest bonus. Total: ~$310k–$500k / year.
• Senior Software Engineer: $235k–$295k base, $400k–$750k equity. Total: ~$450k–$700k / year.
• Staff Engineer: $300k–$370k base, $800k–$1.5M equity. Total: ~$660k–$1M+ / year.

Private-company equity valued at 2025 marks. 4-year vest with 1-year cliff. Expected equity value is meaningful given the AI-infrastructure tailwinds and rapid-funding trajectory; treat as upper-mid upside with meaningful illiquidity risk and competitive-market risk. Cash comp is competitive with top AI-infrastructure companies.

Culture & Work Environment

Academic-adjacent engineering culture — co-founder Ce Zhang is a Stanford / ETH Zurich academic, and Together AI’s research output reflects genuine academic engagement. Vipul Ved Prakash brings startup-execution intensity. The culture combines research-informed thinking with fast execution. SF HQ has significant in-person presence; remote hiring is active. Pace is fast but not frenetic — the AI-infrastructure tailwinds have driven growth while maintaining technical rigor. On-call for inference services is serious.

Things That Surprise People

• The research output is genuine. Together AI publishes inference-optimization and training papers regularly.
• The competitive dynamic (Modal, Replicate, Baseten, Anyscale, cloud-provider offerings) is fierce; speed and quality both matter.
• Enterprise deployment is a real revenue driver, not just marketing. The self-hosted and dedicated-endpoint offerings represent substantial engineering investment.
• The open-source orientation shapes priorities — Together focuses on serving open models well rather than building proprietary ones.

Red Flags to Watch

• Weak CUDA / ML-systems knowledge for inference-engine roles.
• No engagement with the research literature when applying for research-adjacent roles.
• Treating this as a generic “cloud GPU” company. The optimization depth is differentiated.
• Hand-waving on LLM-specific concerns in system design.

Tips for Success

• Know inference optimization. vLLM, FlashAttention, speculative decoding, quantization — vocabulary for system design.
• Use Together AI. Run real inference workloads across several models. Form opinions about performance and API design.
• Read the research papers. Together’s publications plus canonical inference-optimization papers.
• Engage with open-source orientation. Why open over closed? What are the trade-offs?
• Prepare enterprise-customer stories if relevant. The enterprise-deployment business is substantial.

Resources That Help

• Together AI engineering blog and research publications
• vLLM paper (PagedAttention) and documentation
• FlashAttention papers (1, 2, 3) by Tri Dao
• Speculative decoding papers (original and follow-ons)
• Programming Massively Parallel Processors (Hwu, Kirk, Hajj)
• Together AI’s API — run real workloads before interviewing

Frequently Asked Questions

See also: Modal Interview Guide • Replicate Interview Guide • Nvidia Interview Guide

What the Role Pays and How to Weigh the Equity

Cash at a company like Together AI usually lands below what the largest public tech companies pay for the same level, and the gap is meant to be made up in equity. At an early-stage or scaleup company you are most likely to be offered stock options rather than RSUs, though later-stage rounds sometimes shift to RSUs once a valuation is well established. With options you get the right to buy shares at a fixed strike price set when the grant is approved; the bet is that the company is worth far more than that strike by the time you can sell. Options can also expire or force a tax bill when you exercise, so read the grant terms instead of treating the headline share count as money in hand. Confirm current base, bonus, and grant ranges against live sources like Levels.fyi or recent offers before you anchor on any figure.

The number on the offer letter is the optimistic case. Your slice gets diluted every time the company raises another round, so a percentage today shrinks as the company grows. The shares are illiquid until an acquisition or IPO, which may be years out or may never come, and a four-year grant assumes you stay the whole time. Weigh the grant by what you think the company is actually worth now and what you believe about its odds, not by multiplying the share count against a hoped-for future valuation. The real upside is genuine: a strong outcome at the right stage can dwarf a big-tech salary. The real risk is that the equity is worth nothing, and that outcome is common.

When you compare against a big-tech offer, line up the pieces that are actually comparable. Big-tech RSUs vest into liquid stock you can sell on a public market, so that equity is close to cash. Startup options are a wager with a strike price, a tax cost, and no buyer until a liquidity event. A larger paper number from the startup does not mean a larger real number once you discount for risk and the years until you can sell anything.

Lever	What to ask for and why
Equity vs cash mix	If you can absorb the risk, push for more equity; if you need certainty, trade equity back for base. Ask which they have room to move.
Refresh grants	Initial grants often front-load. Ask about the refresh policy so your equity does not flatten out after the first grant vests.
Vesting cliff	The standard one-year cliff means leaving early forfeits everything. Ask whether it can shorten, and clarify acceleration terms on an acquisition.
Strike and exercise	Confirm the strike price, the exercise window if you leave, and whether early exercise is allowed; these decide your tax exposure.